Award Abstract #9702860
CAREER: Parameter Estimation for Ill-Conditioned Systems with Application to Electric Drives and Power Systems

NSF Org:	ECCS Division of Electrical, Communications and Cyber Systems
Initial Amendment Date:	May 20, 1997
Latest Amendment Date:	August 11, 2003
Award Number:	9702860
Award Instrument:	Standard Grant
Program Manager:	Radhakisan S. Baheti ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering
Start Date:	June 1, 1997
Expires:	May 31, 2004 (Estimated)
Awarded Amount to Date:	$500000
Investigator(s):	Miguel Velez-Reyes mvelez@ece.uprm.edu (Principal Investigator)
Sponsor:	University of Puerto Rico Mayaguez Call Box 9000 Mayaguez, PR 00681 787/831-2065
NSF Program(s):	EXP PROG TO STIM COMP RES, CONTROL, NETWORKS, & COMP INTE
Field Application(s):	0306000 Energy Research & Resources
Program Reference Code(s):	OTHR, 9150, 1187, 1076, 1045, 0000
Program Element Code(s):	9150, 1518

ABSTRACT

ECS-9702860 Velez-Reyes The research plan is focused in the area of parameter estimation for ill-conditioned dynamical systems and its application to electric drives and power systems. Physical restrictions in electric drives and power systems limit the quality of the data available for parameter estimation. Therefore, making the associate parameter estimation an ill-conditioned one. 111-conditioning prevents uniformly good estimates of all system parameters from being obtained. This research proposes to develop sound methodologies to deal with ill-conditioned parameter estimation problems in electric drives and power systems. We first, propose to develop methodologies to quantify the severity of the ill-conditioning. Second, we propose the development of parameter estimation algorithms that can be applied to ill-conditioned problems producing physically meaningful parameter estimates, The importance of developing robust parameter estimation algorithms that can deal with low quality data is evident from the need of accurate power system parameters for system simulation, stability analysis, optimization, state estimation, and control. This is becoming even more important with deregulation and the need of operating power systems near their full capacity with lower safety/reserve margins. In the area of electric drives, the strong growth of sensorless control have prompted the need to develop robust parameter estimation algorithms since most approaches to determine mechanical position or speed depend heavily on accurate machine parameters. An important focus of this work is the computational efficiency of algorithms to be developed for condition assessment and for parameter estimation because of the large scale of power systems and the constraints in real-time implementation in electric drive applications. Efficient algorithm development for large scale problems has been a focus of the High Performance Computing (HPC) initiative under the Puerto Rico NSF-EPSCOR program in the past and HPC is still a main trust area of scientific development in the island. The addition of this research program to the HPC effort will strengthen the applications component of this area contributing to the attainment of PR-EPSCOR program goals. The activities proposed in the educational plan will impact undergraduate, graduate, and high school students. Four major activities will be undertaken as part of the educational plan: Revision and creation of courses at the graduate and advanced senior level in the areas of modeling and control of electric drives and power systems, Revision of the Undergraduate Power Systems Laboratory and its integration into undergraduate courses in power systems. Mentoring of graduate and undergraduate students and their engagement in graduate and undergraduate level research projects. Enhancement to the Introduction to Power Systems Workshop offered by the PI to high school students participating at the pre-engineering summer program at UPRM. The curriculum development component of the education plan builds on activities in skill-based and practice-based education including the use of the cooperative learning and teaching methodologies and novel assessment techniques in which the researcher has been involved as part of the NSF-funded Manufacturing Engineering Education Partnership. The courses will be revised to integrate the research work into the classroom. The pre-engineering workshop will be revised in format and content and the materials developed will be disseminated to other institutions and high school teachers. The workshop and high-school teacher activities will be done in collaboration with the UPR Resource Center for Science and Engineering partly funded by NSF.

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